Sentence Centrality Revisited for Unsupervised Summarization
@article{Zheng2019SentenceCR, title={Sentence Centrality Revisited for Unsupervised Summarization}, author={Hao Zheng and Mirella Lapata}, journal={ArXiv}, year={2019}, volume={abs/1906.03508} }
Single document summarization has enjoyed renewed interest in recent years thanks to the popularity of neural network models and the availability of large-scale datasets. In this paper we develop an unsupervised approach arguing that it is unrealistic to expect large-scale and high-quality training data to be available or created for different types of summaries, domains, or languages. We revisit a popular graph-based ranking algorithm and modify how node (aka sentence) centrality is computed…
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